BUSINESS ANALYTICS FOR SALES AND MARKETING MANAGERS PDF

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Business Analytics for Sales and Marketing Managers: How to Compete in the Information Age. Editor(s). Gert H.N. Laursen. First published Business Analytics for Sales and Marketing Managers: How to Compete in the Information Age [Gert H. N. Laursen] on abtresdeorebgolf.tk *FREE* shipping on. SaleS and. Marketing. ManagerS gert H. n. Laursen. BuSineSS. analyticS for. HOW TO COMPETE IN. THE INFORMATION AGE.


Business Analytics For Sales And Marketing Managers Pdf

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Expert guidance on information management for optimum customer intelligence processes Providing essential guidance for information management, this book. Using analytics to identify valuable business opportunities from the data to Director. Jesko is the global knowledge leader of the Marketing & Sales practice . Robert Shaw, Honorary Professor of Marketing Analytics, Cass Business This book must be a reference to practitioners and managers, as well as 28 Predictive sales analytics 29 Customer profitability analytics 30 Mining%abtresdeorebgolf.tk 34 Part One: Ba re analytics Sentiment analysis 9 What is it?.

The truth is: Business Intelligence should be an integral part of your operation. As we all know, information does not necessarily equal intelligence!

The technical backbone to intelligent decision-making is a single, centralized repository that draws together data on all of your business activities and customer interactions. A well-implemented CRM acts as the bridge between teams and will enable you to run reports that deliver a range of key business metrics — on productivity, staff performance, product preferences, sales cycles, customer behaviour, core customers, revenues and market trends — all ready for analysis by the management team.

Business Analytics for Sales and Marketing Managers: How to Compete in the Information Age

Any resulting decisions will be based on hard facts rather than guesswork or assumptions. Smash Sales And Marketing Goals Your Business Intelligence programme will deliver in-depth analysis to kickstart sales, boost the performance of your marketing function, and — crucially — shake up the way that both teams work together.

Providing your salespeople with tools that can measure their activity and identify trends in customer behaviour is fundamental if they are to time their approaches more strategically and exploit all up-selling or cross-selling opportunities.

The company has created dashboards of bespoke indicators including sales leads by stage, to give a visual representation of their cold, warm and hot prospects. The biggest difference is the detailed insight into our sales pipeline which has improved forecasting, work scheduling and process analysis. When it comes to marketing, your team will benefit greatly from BI through improved visibility of sales information, which can be used to finely tune and target their marketing campaigns.

They can use technical features to track and measure every campaign and use information garnered to ensure future marketing initiatives are as profitable and effective as possible.

This is also not a book on data mining. There are many good data mining books, some of which are written for data miners and computer scientists, others for practitioners. However, most of these books focus on algorithms and computing.

That is, they emphasize the many different algorithms that exist in order to extract similar information from the same set of data. This book does not emphasize algorithms. In fact, it acknowledges early on that while there may exist many different ways to solve and tackle a particular problem, the goal is to convey only the main principles of how to discover new knowledge from data and how to make data-driven decisions in a smart and informed way. And finally, this is also not a book on software.

While this book provides in its final chapter a quick-start to one of the most powerful software solutions, emphasis is placed on conveying data-driven thinking and not so much on implementation. The ideas discussed in this book can be implemented using many different software solutions from many different vendors. In fact, this book purposefully steers clear of software implementation since it is our experience that books that do discuss software often place too much emphasis on implementation details, which confuses readers and distract them from the main point.

After all, the main point of this book is not to train new statisticians or data miners — there are better books that can accomplish that goal. The main point is to convey the use and value of data-driven decision making to managers.

Managers hardly ever implement complex methods vii viii Preface and models themselves — however, they frequently communicate with personnel who do. After all, we are living in an information-based society, and using that information smartly can have benefits to both the business and the consumer.

Marketing intelligence

January Wolfgang Jank Contents 1 Introduction. Model Fit. More and more companies are amassing larger and larger amounts of data, storing them in bigger and bigger databases. Every day, telephone companies are collecting several terabytes of data about who we call, when we call them, and how long we talk to them.

Every time we scan our loyalty card at a grocery store, we provide valuable information about the products we like, when we consume them, and the price we are willing to pay for them.

In fact, data collection has become particularly valuable for understanding the relationship between price and demand. The Internet is a particularly convenient place for data collection: every time we click on a link or visit a new Website, we leave a digital footprint e.

Take the example of Internet search engines such as Google or Yahoo. Then, sorting through the network of relationships among millions and millions of Websites, it returns the most relevant results every time we 1 See online.

In fact, Google mines the billions of searches it receives every day in such an efficient way that it can anticipate what we are searching for before we type it and often automatically corrects our spelling. Internet search engines are a particularly compelling example of the power of information: every time we submit a search to Google, we tell it our most private secrets.

So in practical terms, how would Business Intelligence deliver benefits to your company?

In fact, we may tell it that we are looking for a new job or that we are seriously ill — Google knows about it even when no one else in the world does! While such information can be subject to abuse when placed into the wrong hands, it can lead to benefits for the entire society.

For instance, Google mines searches related to the flu2 and is able to anticipate outbreaks earlier than conventional methods, which can help policy makers in epidemiology or health care make timelier and more accurate decisions. Data mining is particularly important for companies that only operate online such as site or Netflix. The reason is that these companies never meet their customers in person and thus do not have the ability to observe their behavior or directly ask them about their needs.

For instance, it may recommend to us a new book based on other books we have downloadd in the past or a product accessory based on the accessories other customers have bought. Automated and datadriven recommendations also known as recommendation engines3 have become the Holy Grail for many Internet retailers.

Take the example of the credit card industry or other credit-granting industries, such as mortgage and banking or the insurance industry.

Credit card issuers often experience adverse selection5 in the sense that those consumers who want their products most eagerly are often the ones who also carry the highest risk. Indeed, the reason that a person is desperate for a new credit card may be that he has an extremely bad credit score and no other company is willing to issue him a credit card. On the other hand, people who already own two or three credit cards and have a stellar credit score may be rather unlikely to respond to a new credit card offer.

So, do we want that person who responds to our offer in a rather eager and desperate fashion as our new customer? As a new company, it wanted to gain market share quickly. However, there was also a danger that those customers who were willing to switch most quickly were also the most risky ones. In order to respond to these challenges, Capital One created a new and innovative, at that time information-based strategy in which they conducted thousands of laboratorylike experiments in order to better understand what characteristics distinguish good customers from bad.

For instance, a customer responding via phone would be flagged as a little more risky than one who assembled a written response sent via regular mail. Successful applications of data-driven decision making in business are plentiful and are increasing on a daily basis.

Using that net worth analysis, they create custom advertising messages and special offer packages for each customer. By analyzing sentiments e. This could be particularly important when products face problems e.

The list of successful data mining stories goes on. Automated and data-driven fraud detection is also popular with credit card companies such as Visa and Mastercard.

Large accounting companies such as PriceWaterhouse develop data-driven methods to unearth inconsistencies in accounting statements. And the list goes on. More curious examples include human resource management at successful sports teams. For instance, both the Boston Red Sox baseball and the New England Patriots football are famous for using data analytics to make decisions about the composition of their teams.

All of this shows that data can play a key role and can provide a competitive edge across many different sectors and in many different business processes both internal and external. Thus, the goal of this book is to convey the value of data-driven analytics 6 See, 4 1 Introduction to managers and business students. This book is very hands-on and practiceoriented. In order to measure the quality of production, the way the employees are behaving with customers, quality of facilities being provided; retailers and service providers send mystery shoppers.

Firms can also assess the quality of customer experience with the shops with the use of mystery shoppers. Competitive intelligence describes the broader discipline of researching, analyzing and formulating data and information from the entire competitive environment of any organization. This can be done by downloading the competitor's products, checking the advertising campaigns, the press media coverage, reading their published reports, etc.

Business Analytics for Managers (Use R)

Competitive intelligence must be legal and ethical. They can be the company's largest customers or representatives of customers or the most outspoken customers. Many business schools set up panels consisting of alumni who provide their knowledge and expertise and help in constituting the course curriculum.

All this data must be or can be referred to as base data. It can help in planning and formulating policies for the companies. For example, television channels will require information on the number of viewership, ratings of TV programs, etc.

An agency which calculates this information and generates this data will provide it to companies that need it. Ultimately it's the customer who's downloading the product.But innovation, product or service development or strategic changes can backfire because of faulty assumptions or information or your cus- tomers may simply not react the way you expect.

In other words when the person who uploaded the image uploaded the image they added descriptor key words to the image to help people find it. Consider what areas in your business you are keen to understand more about and focus on those key analytic sections first.

Alexa Actionable Analytics for the Web. Further reading and references A lot of the information in this section is based on the great information provided on the Mind Tools website: Deployment The final stage of data mining involves using the model selected as being the best from the previous stage and applying it to new data in order to generate predictions or estimates of the expected outcome. Unleashing powerful algorithms and methods on such data can have detrimental results, ranging from inaccurate predictions to complete meaninglessness of our results.

Further reading and references To learn more about factor analysis and how to use it see for example:

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